Social Anonymity is a serious threat to users of mobile social networks. Anonymizing genuine IDs among neighbor nodes explains such concerns. Be that as it may, this prevents nodes from gathering genuine ID-based encountering data, which is expected to help mobile opportunistic social networks services. Along these lines, in this paper, This work Propose that scalable fine grained approach that can support both anonymizing genuine IDs among neighbor nodes furthermore, collecting genuine ID-based encountering data. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, every node sends encrypted encountering evidence to the encountered node to enable encountering data collection. A collection of novel methods are intended to guarantee the confidentiality and uniqueness of encountering proofs. A scalableapproach additionally supports fine-grained control over what data is shared to the encountered node in light of attribute similarity (i.e., trust), which is measured without revealing attributes. Keywords: Mobile Opportunistic Social Networks, Anonymity, Encountering Information
C.xiao  propose an top-k similarity joins to detect a near duplicate data for a large-scale real datasets. This algorithm can be used in pattern recognition, page detection and data integration and an efficient result can be produced. The prefix filtering principle is used to determine the upper bounding of similarity values and an scalableapproach to identify the threshold value to determine the window size. Threshold can be determined automatically based on the parameter used in the approaches.
A significant attribute of scholars programs is that they inherently provide the student with an identity or affili- ation with a group (e.g., Meyerhoff Scholar). A similar effect in a scalable fashion is being obtained through the establishment of a STEM Living and Learning Community (LLC) at UMBC. The STEM LLC is a critical component of the BTP, as it is required for two of the three groups in the study. The STEM LLC encourages camaraderie among its residents, exposes them to students pursuing other STEM majors, expands perspectives, offers peer mentoring, encourages participation in applied learning experiences, develops leadership skills, fosters study groups participa- tion, and fosters cultural/artistic appreciation. The 2007 National Study of Living-Learning Programs (NSLLP) found that participants in LLCs had a higher mean score in regard to feeling that they had a smoother academic and social transition to college, and felt a stronger sense of be- longing . These results confirm findings from many
Scalability is a key concern for SMP based architecture in the current context. NUMA based architecture design seems to be a promising hope addressing the scalability. At the same time CC-NUMA based design architecture demands a deeper understanding and open vistas for key areas of improvement. With the approach of many core environments where memory is distributed among the different cores, it is challenging to design a thread scheduler along with proper data distribution across different nodes in a productive way. Our proposed research tries to investigate, evolve and analyze one of the key design issues for NUMA machine and proposes an innovative solution to address this key design issue under investigation in the current phase of our work. Our Algorithmic design proposed seems to be outperforming with respect to LibNUMA specifically.
Clustering is a popular problem with many applications. We consider the k-means problem in the situation where the data is too large to be stored in main memory and must be accessed sequentially, such as from a disk, and where we must use as little memory as possible. Our algorithm is based on recent theoretical results, with significant improvements to make it practical. Our approach greatly simplifies a recently developed algorithm, both in design and in analysis, and eliminates large constant factors in the approximation guarantee, the memory requirements, and the running time. We then incorporate approximate nearest passenger search to compute k- means in o(nk) (where n is the number of data points; note that computing the cost, given a solution, takes 8(nk) time). We show that our algorithm compares favorably to existing algorithms - both theoretically and experimentally, thus providing state-of-the-art performance in both theory and practice. 
levels of performance relative to real world time. As simulation scale increases in terms of required infrastructure scale and simulation complexity, performance becomes an increasing concern for both users and system administrators in terms of business requirements and operational costs, respectively. To facilitate this, Service-oriented Architecture (SOA) - which supports open standards, loose coupling, discoverability and reusability for the integration of distributed systems  - appears to be a promising approach in providing an effective means to address the above criteria. This is particularly true for reducing the coupling between simulation and the underlying hardware, as well as transforming distributed simulators into SaaS offerings which can be accessed through generic protocols globally. This paper presents SEED (Simulation EnvironmEnt Distributor), a novel service-oriented approach for simulating CPSs across a distributed system. This is achieved through automated simulation partitioning, instantiation and execution across a loosely-coupled infrastructure while enforcing tight event messaging across the system. Users are capable of interacting with SEED as a Windows Communication Foundation (WCF) Web Service using XML-based protocols, and are not required to configure underlying hardware for execution. As a result, SEED operates efficiently within both small and large-scale OTS hardware, agnostic of cluster heterogeneity and OS
The outcomes can be utilized as a part of numerous ranges, for example, creating Web page suggestion, item proposal, content change, or page removal. In this review, a procedure for fusing semantic data into regular route design extraction is proposed and the impact of semantic data on example quality is expounded. By presenting semantic information, Web use mining examples are created as far as metaphysics cases of Web page addresses. Such examples can mirror the semantics of route conduct more expressly and precisely. The impact of semantic data on example quality is assessed through a suggestion structure. Proposals are produced by either considering the continuous route designs regarding a solitary idea or considering blend of successive route designs regarding a few ideas. Test comes about demonstrate that coordinating semantic data gives deliberation that outcome in impressive change of example quality. What's more, this approach handles new thing issue in proposal .
Renesse, Minsky and Hayden described the first gossip based failure detection service in Ref . In gossip systems, a give node gossips (and passes information) to a set of randomly selected nodes. Gossip system tends to scale well and have no single point of failures. However, systems based on gossip schemes need to address the consistency issue which results from uneven propagation of the gossips. The GEMS (Gossip Enabled Monitoring Service)  system provides a scaleable resource monitoring service. Nodes within the GEMS system gossip with each other about information related to resource monitoring. The approach taken here is that of a layered gossip scheme, where nodes are organized into gossip trees. Since gossiping can sometimes lead to uneven spread of failure information, the system relies on consensus: a majority is needed for deeming a failure.
Abstract. Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resem- bling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we intro- duce an incremental, scalableapproach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filter- ing and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the pro- jection stage consisting of document clustering, cluster force-directed place- ment and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indi- cate that our incremental computation approach is capable of accurately gene- rating dynamic information landscapes.
In this paper, we focus on Elliptic Curve Cryptography based approach for Secure Multiparty Computation (SMC) problem. Widespread proliferation of data and the growth of communication technologies have enabled collaborative computations among parties in distributed scenario. Preserving privacy of data owned by parties is crucial in such scenarios. Classical approach to SMC is to perform computation using Trusted Third Party (TTP). However, in practical scenario, TTPs are hard to achieve and it is imperative to eliminate TTP in SMC. In addition, existing solutions proposed for SMC use classical homomorphic encryption schemes such as RSA and Paillier. Due to the higher cost incurred by such cryptosystems, the resultant SMC protocols are not scalable. We propose Elliptic Curve Cryptography (ECC) based approach for SMC that is scalable in terms of computa- tional and communication cost and avoids TTP. In literature, there do exist various ECC based homomorphic schemes and it is imperative to investigate and analyze these schemes in order to select the suitable for a given application. In this paper, we empirically analyze various ECC based homomorphic encryption schemes based on performance metrics such as computational cost and communication cost. We recommend an efficient algo- rithm amongst several selected ones, that offers security with lesser overheads and can be applied in any applica- tion demanding privacy.
Computational power needs have grown dramatically in recent years. This is also the case in many language processing tasks, due to overwhelming quantities of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the NLP field. In this paper we describe a series of experiments carried out in the context of the NewsReader project with the goal of analyzing the scaling capabilities of the language processing pipeline used in it. We explore the use of Storm in a new approach for scalable distributed language processing across multiple machines and evaluate its effectiveness and efficiency when processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding language processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing nodes.
There is also a line of prior work on enhanc- ing sequential text classification or tagging. Hier- archical character-to-word level LSTM (Hochre- iter and Schmidhuber, 1997) architectures similar to our models have been explored for the Named Entity Recognition task by Lample et al. (2016). Character-informed sequence models have also been explored for simple text classification with small sets of classes by Xiao and Cho (2016). Joulin et al. (2016) explored highly scalable text classification using a shared hierarchical encoder, but their hierarchical softmax-based output formu- lation is unsuitable for incremental model updates. Work on zero-shot domain classifier expansion by Kumar et al. (2017b) struggled to rank incom- ing domains higher than training domains. The attention-based approach of Kim et al. (2017d) does not require retraining from scratch, but it requires keeping all models stored in memory which is computationally expensive. Multi-Task learning was used in the context of SLU by Tur (2006) and has been further explored using neu- ral networks for phoneme recognition (Seltzer and Droppo, 2013) and semantic parsing (Fan et al., 2017; Bapna et al., 2017). There have been many other pieces of prior work on improving NLU sys- tems with pre-training (Kim et al., 2015b; Celiky- ilmaz et al., 2016; Kim et al., 2017e), multi-task learning (Zhang and Wang, 2016; Liu and Lane, 2016; Kim et al., 2017b), transfer learning (El- Kahky et al., 2014; Kim et al., 2015a,c; Chen et al., 2016a; Yang et al., 2017), domain adaptation (Kim et al., 2016; Jaech et al., 2016; Liu and Lane, 2017; Kim et al., 2017d,c) and contextual signals (Bhar- gava et al., 2013; Chen et al., 2016b; Hori et al., 2016; Kim et al., 2017a).
Whereas a major raison d ’ être of formal ontologies is to promote an unambiguous and commonly agreed typology for categorizing the entities of the domain to be repre- sented, the representation of diseases in biomedical ontologies is widely idiosyncratic. Current biomedical ontologies and terminology systems subscribe to diverging and partly contradicting concepts regarding diseases. Diseases have often been categorized as processes, events, or states ; a recent proposal regards diseases, first of all, as dis- positional entities . This approach has been further refined by the Ontology of General Medical Science (OGMS) , with the consideration of disorders as the abnor- mal bodily components and disease processes as the manifestations of diseases (dispositions).
centers. Key management in the ad hoc network is a difficult issue concerning the security of the group communication. This paper gave an approach for the design and analysis of region-based key management protocols for scalable and reconfigurable group key management in MANETs. The proposed simple efficient region-based group key management protocol divides a group into region-based subgroups based on decentralized key management principles by using the efficient Region based group key management Protocol (SERGK). In this approach, there is one group member that acts as a group coordinator which computes and distributes the blinded intermediate keying information the group. Every member computes the group key in a distributed manner. To distribute the workload of group rekeying and maintenance, the role of group coordinator is rotated among all members. A new key tree structure is introduced in order to switch the group control role efficiently. A simulation study has been conducted to compare the message cost and computation cost under group key management schemes and it is found that the proposed approach performs better than the present RGK approach.
Bottom-Up Generalization is an efficient k-anonymization method. In a k-anonymous data set, each record is indistinguishable from at least k−1 other records with respect to QID. Basically, Bottom-Up Generalization (BUG) approach of anonymization is an iterative process starting from the lowest anonymization level. We leverage the information/privacy trade- off as the search metric for our approach, i.e., the Information Loss per Privacy Gain (ILPG). In this section we elaborate the BUG and MRBUG Driver. The BUG consists of following steps, data partition, run the MRBUG Driver on partitioned data set, combining the anonymization levels of the partitioned data set and applying generalization to original data set with integrated anonymization level without violating the k-anonymity.
In this paper, an efficient approach for joint source and channel coding is presented. The proposed approach exploits the scalable video/image coding and the adaptive PUM turbo codes. UEP is used to minimize the end-to-end distortion by considering the channel rate, packet size of turbo code and interleaver at given channel conditions and with limited complexity. The performance using PUM turbo code as the error protection code is also evaluated. Experimental results show efficiency of the proposed solution at low complexity.
5.2 The Effectiveness of the Joint Model Our experimental results are shown in 3. The left- most part of the table concerns the Royal dataset. We see that the universal schema approach out- performs the MLN baseline in most cases, but ProPPR’s SL method substantially improves over MLN’s conjugated gradient learning method, and the universal schema approach. This is perhaps surprising, as the universal schema approach is also a joint method: we note that in our datasets, unlike the New York Times corpus used in (Riedel et al., 2013), large numbers of unlabeled examples are not available. The unigram and bilexical IE models in ProPPR also perform well—better than SL on this data. The joint model outperforms the baselines, as well as the separate models. The dif- ference is most pronounced when the background KB gets noisier: the improvement with 10% miss- ing setting is about 1.5 to 2.3% MAP, while with 50% missing data, the absolute MAP improve- ment is from 8% to 10%.
This proposed scheme has a novel approach of scalable coding for encrypted images and image compression using Absolute Moment block truncation coding has been examined The original image is encrypted by a modulo 256 addition with (PN) pseudorandom numbers, and the encoded bit streams are made a quantized encrypted sub image and the quantized remains of Harr coefficients. At the receiver, whereas the sub image is decrypted to create an approximate image, the quantized data of Harr coefficients can be offered more exhaustive data for image reconstruction. Meanwhile the bit streams are made with a multiple-resolution construction, the principal content with higher resolution obtained while more bit streams are received. The lossy compression AMBTC and scalable coding for encrypted image with better performance merits more analysis in the future. REFERENCES
This clustering algorithm and protocol are mainly due to our mobility prediction model as explained in .The Mobility Trie that each MN constructs plays an important role to bring in proactivness in our (b,e,m)- clustering approach. Each MN „A‟ to determine its residence-time (tav) in virtual cluster „V‟ from its Mobility Trie. The MN that has the highest Ω(from equ.) can become the primary CH, and the MNs that have the second and third highest values become assistant(secondary) CHs. To form clusters, CH should cover the whole area of virtual cluster .So CH makes a k-hop cluster where value „k‟ is not necessarily Uniform However, efforts have been made to limit the value of „k‟, as it is better if every MN is only a fewer hops for proper management.